Discovering Business Processes in CRM Systems by leveraging unstructured text data

Recent research has proven the feasibility of using Process Mining algorithms to discover business processes from event logs of structured data. However, many IT systems also store a considerable amount of unstructured data. Customer Relationship Management (CRM) Systems typically store information about interactions with customers, such as emails, phone calls, meetings, etc. These activities are characteristically made up of unstructured data, such as a free text subject and description of the interaction, but only limited structured data is available to classify them. This poses a problem to the traditional Process Mining approach that relies on an event log made up of clearly categorised activities. This paper proposes an original framework to mine processes from CRM data, by leveraging the unstructured part of the data. This method uses Latent Dirichlet Allocation (LDA), an unsupervised machine learning technique, to automatically detect and assign labels to activities. This framework does not require any human intervention. A case study with real-world CRM data validates the feasibility of our approach

Enabling Process Mining in Airbus Manufacturing : Extracting Event Logs and Discovering Processes from Complex Data

Ministerio de Ciencia y Tecnología RTI2018-094283-B-C3

Social Business Intelligence: a Literature Review and Research Agenda

The domains of Business Intelligence (BI) and social media have meanwhile become significant research fields. While BI aims at supporting an organization’s decisions by providing relevant analytical data, social media is an emerging source of personal and individual knowledge, opinion, and attitudes of stakeholders. For a while, a convergence of the two domains can be observed in real-world implementations and research, resulting in concepts like social BI. Many research questions still remain open – or even worse – are not yet formulated. Therefore, the paper aims at articulating a research agenda for social BI. By means of a literature review we systematically explored previous work and developed a framework. It contrasts social media characteristics with BI design areas and is used to derive the social BI research agenda. Our results show that the integration of social media (data) into a BI system has impact on almost all BI design objects

Enabling data-driven decision-making for a Finnish SME: a data lake solution

In the era of big data, data-driven decision-making has become a key success factor for companies of all sizes. Technological development has made it possible to store, process and analyse vast amounts of data effectively. The availability of cloud computing services has lowered the costs of data analysis. Even small businesses have access to advanced technical solutions, such as data lakes and machine learning applications. Data-driven decision-making requires integrating relevant data from various sources. Data has to be extracted from distributed internal and external systems and stored into a centralised system that enables processing and analysing it for meaningful insights. Data can be structured, semi-structured or unstructured. Data lakes have emerged as a solution for storing vast amounts of data, including a growing amount of unstructured data, in a cost-effective manner. The rise of the SaaS model has led to companies abandoning on-premise software. This blurs the line between internal and external data as the company’s own data is actually maintained by a third-party. Most enterprise software targeted for small businesses are provided through the SaaS model. Small businesses are facing the challenge of adopting data-driven decision-making, while having limited visibility to their own data. In this thesis, we study how small businesses can take advantage of data-driven decision-making by leveraging cloud computing services. We found that the report- ing features of SaaS based business applications used by our case company, a sales oriented SME, were insufficient for detailed analysis. Data-driven decision-making required aggregating data from multiple systems, causing excessive manual labour. A cloud based data lake solution was found to be a cost-effective solution for creating a centralised repository and automated data integration. It enabled management to visualise customer and sales data and to assess the effectiveness of marketing efforts. Better skills at data analysis among the managers of the case company would have been detrimental to obtaining the full benefits of the solution

A Customer-Support Knowledge Network Integrating Different Communication Elements for an E-Commerce Portal Using Self Organizing Maps

Successful e-commerce portal organizations focus intensely on customers. They try to consider every bit of information that flows from the customer to their system as an input for analyzing and identifying their needs precisely and catering to them. Being mostly ‘click and mortar’ or completely e-enabled, they have a lot of operational flexibility to address customer requirements in a more personalized and customized way than their brick-andmortar counterparts with more operational rigidity and resource constraints. Managing diverse range of channels is a challenge because of exponential and sometimes disruptive growth of diverse technologies that are used for supporting highvolume e-commerce operations. Customers are bouncing between phone, email and the web with greater fluidity than ever and therefore, fragmented, ‘stove-pipe’ communications, in such situations, can create problems as they loose out the holistic view on the basic nature of the problems and customer priorities. Therefore, the use of a common knowledge base across all channels is a dire necessity for an e-commerce portal, especially the ones which do not have a ‘brick-and-mortar’ back-end. The customer-support knowledge network as proposed in this paper addresses these issues. Using Self Organizing Maps(SOM), the network becomes incrementally self learning representing various groups of communication instances at any point of time. The advantages include the integration of all communication elements and an assimilation of all the customer communication issues into a reusable form of self-learning network. It adds an immense value for a customer-focused ecommerce company for identification of generic issues, better understanding of customer concerns and priorities and designing products/ services/ promotions accordingly, to ensure an overall better success of business

Enabling Process Mining in Aircraft Manufactures: Extracting Event Logs and Discovering Processes from Complex Data

Process mining is employed by organizations to completely understand and improve their processes and to detect possible deviations from expected behavior. Process discovery uses event logs as input data, which describe the times of the actions that occur the traces. Currently, Internet-of-Things environments generate massive distributed and not always structured data, which brings about new complex scenarios since data must first be transformed in order to be handled by process min ing tools. This paper shows the success case of application of a solution that permits the transformation of complex semi-structured data of an assembly-aircraft process in order to create event logs that can be man aged by the process mining paradigm. A Domain-Specific Language and a prototype have been implemented to facilitate the extraction of data into the unified traces of an event log. The implementation performed has been applied within a project in the aeronautic industry, and promis ing results have been obtained of the log extraction for the discovery of processes and the resulting improvement of the assembly-aircraft process.Ministerio de Ciencia y Tecnología RTI2018-094283-B-C3

A Survey on Actionable Knowledge

Actionable Knowledge Discovery (AKD) is a crucial aspect of data mining that is gaining popularity and being applied in a wide range of domains. This is because AKD can extract valuable insights and information, also known as knowledge, from large datasets. The goal of this paper is to examine different research studies that focus on various domains and have different objectives. The paper will review and discuss the methods used in these studies in detail. AKD is a process of identifying and extracting actionable insights from data, which can be used to make informed decisions and improve business outcomes. It is a powerful tool for uncovering patterns and trends in data that can be used for various applications such as customer relationship management, marketing, and fraud detection. The research studies reviewed in this paper will explore different techniques and approaches for AKD in different domains, such as healthcare, finance, and telecommunications. The paper will provide a thorough analysis of the current state of AKD in the field and will review the main methods used by various research studies. Additionally, the paper will evaluate the advantages and disadvantages of each method and will discuss any novel or new solutions presented in the field. Overall, this paper aims to provide a comprehensive overview of the methods and techniques used in AKD and the impact they have on different domains

Business intelligence and big data in hospitality and tourism: a systematic literature review

Purpose This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research. Design/methodology/approach The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization. Findings Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research. Research limitations/implications This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed. Originality/value This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data